CN111062316A - Pollution source wastewater discharge real-time video analysis system based on deep learning technology - Google Patents
Pollution source wastewater discharge real-time video analysis system based on deep learning technology Download PDFInfo
- Publication number
- CN111062316A CN111062316A CN201911288393.XA CN201911288393A CN111062316A CN 111062316 A CN111062316 A CN 111062316A CN 201911288393 A CN201911288393 A CN 201911288393A CN 111062316 A CN111062316 A CN 111062316A
- Authority
- CN
- China
- Prior art keywords
- image
- wastewater
- deep learning
- analysis system
- pollution source
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000002351 wastewater Substances 0.000 title claims abstract description 48
- 238000004458 analytical method Methods 0.000 title claims abstract description 21
- 238000013135 deep learning Methods 0.000 title claims abstract description 15
- 238000005516 engineering process Methods 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 32
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 31
- 238000000605 extraction Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 15
- 239000010865 sewage Substances 0.000 claims abstract description 15
- 238000001514 detection method Methods 0.000 claims abstract description 8
- 239000003344 environmental pollutant Substances 0.000 claims abstract description 8
- 231100000719 pollutant Toxicity 0.000 claims abstract description 8
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 description 3
- 241000282414 Homo sapiens Species 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004141 dimensional analysis Methods 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000006260 foam Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 239000008258 liquid foam Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Multimedia (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Health & Medical Sciences (AREA)
- Signal Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a pollution source wastewater discharge real-time video analysis system based on a deep learning technology, which at least comprises an image acquisition unit and a data processing unit, wherein the image acquisition unit is arranged at each monitoring point of a region to be monitored and is used for completing wastewater image acquisition of each monitoring point; the data processing unit is used for finishing the identification of illegal sewage discharge conditions in the wastewater of each monitoring point based on a plurality of image information data acquired by the image acquisition unit and the 3D CNNs comprising a two-dimensional space domain and a one-dimensional time domain. The system can complete the whole image full-scene analysis through the image extraction module, and positions the discharge port through a target detection algorithm without manually checking a specific area. Meanwhile, continuous multi-frame images on a video image time domain are analyzed in the target monitoring module through the 3D convolutional neural network, and the mobility characteristics of pollutants at the discharge port can be analyzed, so that the accuracy of illegal wastewater pollution discharge identification is improved.
Description
Technical Field
The invention belongs to the technical field of pollution source emission monitoring, and particularly relates to a pollution source wastewater emission real-time video analysis system based on a deep learning technology.
Background
With the rapid development of economy and society, the urbanization construction of China is increasingly accelerated, a large number of people gather in urban life, which causes serious influence on the environment and ecology around the city, and a large number of pollutants appear on the water surfaces of many drinking water sources, urban rivers, surrounding lakes, reservoirs and the like, most of the pollutants are caused by industrial pollution discharge and contain a large amount of substances harmful to water quality.
If the sewage cannot be cleaned in time, the ecological environment is inevitably damaged, the survival and development of human beings are threatened, and in order to realize social sustainable development and human health, the supervision on illegal sewage discharge conditions of pollution source enterprises, such as illegal discharge and leakage of the pollution source enterprises, abnormal color of discharge port waste water, a large amount of foam, abnormal liquid level and the like, must be enhanced. Therefore, the monitoring of illegal pollution discharge conditions of pollution source enterprises becomes a problem which needs to be solved urgently, and a large amount of manpower and material resources need to be invested.
In addition, the existing monitoring method for illegal pollution discharge of the pollution source wastewater is mainly realized by analyzing a single-frame image, the scheme only analyzes a single-frame 2-dimensional image, and the pollutants and the monitored environment have the characteristics of very complex diversity in the aspects of color and form.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a pollution source wastewater discharge real-time video analysis system based on a deep learning technology.
The purpose of the invention is realized by the following technical scheme:
a real-time video analysis system for pollution source wastewater discharge based on a deep learning technology at least comprises an image acquisition unit and a data processing unit, wherein the image acquisition unit is arranged at each monitoring point of a region to be monitored and is used for completing wastewater image acquisition of each monitoring point; the data processing unit is used for finishing the identification of illegal sewage discharge conditions in the wastewater of each monitoring point based on a plurality of image information data acquired by the image acquisition unit and the 3D CNNs comprising a two-dimensional space domain and a one-dimensional time domain.
According to a preferred embodiment, the data processing unit at least comprises an image extraction module, and the image extraction module is used for performing wastewater discharge identification on image information data acquired by the image acquisition unit based on a target detection method; and based on the position information of the wastewater discharge port in the image, the wastewater image extraction in the discharge port area is completed.
According to a preferred embodiment, the image extraction module uses fast RCNN for object detection to identify and locate the wastewater discharge area in the image.
According to a preferred embodiment, the data processing unit further comprises a water quality classification module, wherein the water quality classification module adopts a Convolutional Neural Network (CNN) to construct a classifier for the extracted images of the discharge port, and performs water quality classification on the discharge port wastewater.
According to a preferred embodiment, the data input layer of the convolutional neural network CNN accesses the real-time image data of the pollution source by using a local sensing and weight sharing mode.
According to a preferred embodiment, the data output layer of the convolutional neural network CNN uses a fully-connected neural network and a cross entropy scoring function to classify the pollution source wastewater.
According to a preferred embodiment, the data processing unit further comprises a target monitoring module, and the target monitoring module adopts a 3D convolutional neural network to realize water quality identification on the wastewater monitoring image of the sewage outlet.
According to a preferred embodiment, the 3D convolutional neural network analyzes and identifies the sewage monitoring image information of the continuous video frames, and finally outputs the color, texture and motion characteristics of the sewage.
The main scheme and the further selection schemes can be freely combined to form a plurality of schemes which are all adopted and claimed by the invention; in the invention, the selection (each non-conflict selection) and other selections can be freely combined. The skilled person in the art can understand that there are many combinations, which are all the technical solutions to be protected by the present invention, according to the prior art and the common general knowledge after understanding the scheme of the present invention, and the technical solutions are not exhaustive herein.
The invention has the beneficial effects that: by the arrangement of the functional modules in the system, the system can complete the whole image full-scene analysis through the image extraction module, and positions the discharge port through a target detection algorithm without manually selecting a specific area. Meanwhile, continuous multi-frame images on a video image time domain are analyzed in the target monitoring module through the 3D convolutional neural network, and the mobility characteristics of pollutants at the discharge port can be analyzed, so that the accuracy of pollution source identification is improved.
Drawings
FIG. 1 is a schematic diagram of the structure of an analysis system of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that, in order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments.
Thus, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in a figure, it need not be further defined and explained later.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations and positional relationships that are conventionally used in the products of the present invention, and are used merely for convenience in describing the present invention and for simplicity in description, but do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, it should be noted that, in the present invention, if the specific structures, connection relationships, position relationships, power source relationships, and the like are not written in particular, the structures, connection relationships, position relationships, power source relationships, and the like related to the present invention can be known by those skilled in the art without creative work on the basis of the prior art.
Example 1:
referring to fig. 1, a pollution source wastewater discharge real-time video analysis system based on deep learning technology is disclosed.
Preferably, the video analysis system comprises at least an image acquisition unit and a data processing unit. The image acquisition unit is arranged at each monitoring point of the area to be monitored and is used for completing the acquisition of wastewater images of each monitoring point. The data processing unit is used for finishing the identification of illegal sewage discharge conditions in the wastewater of each monitoring point based on a plurality of image information data acquired by the image acquisition unit and the 3D CNNs comprising a two-dimensional space domain and a one-dimensional time domain.
Preferably, the image acquisition unit comprises a real-time image acquisition device arranged at each monitoring point.
Preferably, the data processing unit at least comprises an image extraction module, a water quality classification module and a target monitoring module. And completing the extraction of the region of interest of the image information acquired by the image acquisition unit through the image extraction module. And finishing the classification of the images in the induction area through a water quality classification module. And the target monitoring module is used for finishing the identification and classification of the wastewater in the induction area.
Preferably, the image extraction module performs wastewater discharge identification on the image information data acquired by the image acquisition unit based on a target detection method. And based on the position information of the wastewater discharge port in the image, the wastewater image extraction in the discharge port area is completed.
Further, the image extraction module adopts fast RCNN to detect the target, so as to identify and position the wastewater discharge area in the image.
Preferably, the water quality classification module adopts a Convolutional Neural Network (CNN) to construct a classifier for the extracted images of the discharge port, and performs water quality classification on the discharge port wastewater.
Further, the data input layer of the convolutional neural network CNN performs data access by adopting a local sensing and weight sharing mode.
Further, the data output layer of the convolutional neural network CNN is classified by adopting a fully-connected neural network and a cross entropy scoring function.
Preferably, the target monitoring module adopts a 3D convolutional neural network to realize water quality identification on the wastewater monitoring image of the sewage outlet.
Further, the 3D convolutional neural network analyzes and identifies the sewage monitoring image information of the continuous video frames, and finally outputs the color, texture and motion characteristics of the sewage.
According to the system, the whole image full-scene analysis is completed through the image extraction module, the position of the discharge port is positioned through a target detection algorithm, and a specific area does not need to be manually selected. Meanwhile, continuous multi-frame images on a video image time domain are analyzed in the target monitoring module through the 3D convolutional neural network, and the mobility characteristics of pollutants at the discharge port can be analyzed, so that the accuracy of pollution source identification is improved. Meanwhile, the special identification is carried out based on the wastewater discharge port, the motion process of liquid, gas and liquid foam under the discharge port can be effectively identified, the pollutants are identified by combining color analysis, and a multi-dimensional analysis result can be obtained. Meanwhile, real-time image analysis is carried out through multiple dimensions, so that the system has obvious external interference resistance, and is influenced by adverse conditions such as external reflection and weather conditions, and the identification accuracy of the system is greatly improved.
The foregoing basic embodiments of the invention and their various further alternatives can be freely combined to form multiple embodiments, all of which are contemplated and claimed herein. In the scheme of the invention, each selection example can be combined with any other basic example and selection example at will. Numerous combinations will be known to those skilled in the art.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A real-time video analysis system for the wastewater discharge of pollution sources based on a deep learning technology is characterized by at least comprising an image acquisition unit and a data processing unit,
the image acquisition unit is arranged at each monitoring point of the area to be monitored and is used for completing the acquisition of wastewater images of each monitoring point;
the data processing unit is used for finishing the identification of illegal sewage discharge conditions in the wastewater of each monitoring point based on a plurality of image information data acquired by the image acquisition unit and the 3D CNNs comprising a two-dimensional space domain and a one-dimensional time domain.
2. The pollution source wastewater discharge real-time video analysis system based on the deep learning technology as claimed in claim 1, wherein the data processing unit at least comprises an image extraction module,
the image extraction module is used for completing wastewater discharge identification on the image information data acquired by the image acquisition unit based on a target detection method; and based on the position information of the wastewater discharge port in the image, the wastewater image extraction in the discharge port area is completed.
3. The deep learning technique-based real-time video analysis system for pollutant source wastewater discharge of claim 2, wherein the image extraction module performs target detection by using fast RCNN so as to identify and locate the wastewater discharge outlet region in the image.
4. The pollution source wastewater discharge real-time video analysis system based on the deep learning technology as claimed in claim 2, wherein the data processing unit further comprises a water quality classification module,
and the water quality classification module adopts a Convolutional Neural Network (CNN) to construct a classifier for the extracted images of the discharge port and performs water quality classification on the discharge port wastewater.
5. The pollution source wastewater discharge real-time video analysis system based on the deep learning technology as claimed in claim 4, wherein the data input layer of the convolutional neural network CNN accesses the pollution source real-time image data by adopting a local sensing and weight sharing mode.
6. The pollution source wastewater discharge real-time video analysis system based on the deep learning technology as claimed in claim 4, wherein the data output layer of the convolutional neural network CNN adopts a fully connected neural network and a cross entropy scoring function to classify the pollution source wastewater.
7. The pollution source wastewater discharge real-time video analysis system based on the deep learning technology as claimed in claim 4, wherein the data processing unit further comprises a target monitoring module,
and the target monitoring module realizes water quality identification on the wastewater monitoring image of the sewage outlet by adopting a 3D convolutional neural network.
8. The pollution source wastewater discharge real-time video analysis system based on the deep learning technology as claimed in claim 7, wherein the 3D convolutional neural network analyzes and identifies the sewage monitoring image information of the continuous video frames, and finally outputs the color, texture and motion characteristics of the sewage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911288393.XA CN111062316A (en) | 2019-12-16 | 2019-12-16 | Pollution source wastewater discharge real-time video analysis system based on deep learning technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911288393.XA CN111062316A (en) | 2019-12-16 | 2019-12-16 | Pollution source wastewater discharge real-time video analysis system based on deep learning technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111062316A true CN111062316A (en) | 2020-04-24 |
Family
ID=70301776
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911288393.XA Pending CN111062316A (en) | 2019-12-16 | 2019-12-16 | Pollution source wastewater discharge real-time video analysis system based on deep learning technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111062316A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112101790A (en) * | 2020-09-16 | 2020-12-18 | 清华大学合肥公共安全研究院 | Water body monitoring video and data linkage early warning method and system |
| CN113049445A (en) * | 2021-03-22 | 2021-06-29 | 中国矿业大学(北京) | Coal water slurry fluidity detection device based on deep learning and detection method thereof |
| CN113128105A (en) * | 2021-03-31 | 2021-07-16 | 北京工业大学 | Method and device for monitoring sudden watershed water pollution accident |
| CN113343923A (en) * | 2021-07-01 | 2021-09-03 | 江苏舆图信息科技有限公司 | Real-time river drainage port drainage state identification method based on video images |
| CN113588650A (en) * | 2021-09-30 | 2021-11-02 | 武汉市中卫寰宇医疗系统工程有限公司 | Medical sewage purification monitoring system |
| CN113920471A (en) * | 2021-10-13 | 2022-01-11 | 平安国际智慧城市科技股份有限公司 | Remote monitoring method and device for production waste, computer equipment and storage medium |
| CN115100553A (en) * | 2022-07-06 | 2022-09-23 | 浙江科技学院 | River surface pollution information detection processing method and system based on convolutional neural network |
| CN115457485A (en) * | 2022-11-11 | 2022-12-09 | 成都见海科技有限公司 | Drainage monitoring method and system based on 3D convolution and storage medium |
| CN115620243A (en) * | 2022-12-20 | 2023-01-17 | 深圳联和智慧科技有限公司 | Pollution source monitoring method and system based on artificial intelligence and cloud platform |
| CN117475367A (en) * | 2023-06-12 | 2024-01-30 | 中国建筑第四工程局有限公司 | Sewage image processing method and system based on multi-rule coordination |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3050300A1 (en) * | 2016-04-19 | 2017-10-20 | Thales Sa | METHOD AND DEVICE FOR AUTOMATIC DETECTION OF POLLUTION ZONES ON A WATER SURFACE |
| CN108009736A (en) * | 2017-12-13 | 2018-05-08 | 北京北华中清环境工程技术有限公司 | A kind of water quality early-warning and predicting system and water quality early-warning and predicting method |
| CN108007573A (en) * | 2017-12-04 | 2018-05-08 | 佛山市南海区环境保护监测站（佛山市南海区机动车排气污染管理所） | A kind of motor-vehicle tail-gas blackness analysis system and method |
| CN108881857A (en) * | 2018-08-04 | 2018-11-23 | 肖恒念 | Blowdown intelligent control method based on real-time video |
| CN109325403A (en) * | 2018-08-07 | 2019-02-12 | 广州粤建三和软件股份有限公司 | A kind of water pollution identification administering method and system based on image recognition |
| CN109886139A (en) * | 2019-01-28 | 2019-06-14 | 平安科技（深圳）有限公司 | Human testing model generating method, sewage draining exit method for detecting abnormality and device |
| CN110085281A (en) * | 2019-04-26 | 2019-08-02 | 成都之维安科技股份有限公司 | A kind of environmental pollution traceability system and method based on feature pollution factor source resolution |
| KR20190092327A (en) * | 2019-07-18 | 2019-08-07 | 엘지전자 주식회사 | Water purifier and control method thereof |
| CN110220851A (en) * | 2019-06-25 | 2019-09-10 | 生态环境部卫星环境应用中心 | A kind of air pollution emission detection method and system based on unmanned plane |
| US20200125852A1 (en) * | 2017-05-15 | 2020-04-23 | Deepmind Technologies Limited | Action recognition in videos using 3d spatio-temporal convolutional neural networks |
| CN112712008A (en) * | 2020-12-28 | 2021-04-27 | 华川技术有限公司 | Water environment early warning judgment method based on 3D convolutional neural network |
| US20210389293A1 (en) * | 2020-06-12 | 2021-12-16 | Chinese Research Academy Of Environmental Sciences | Methods and Systems for Water Area Pollution Intelligent Monitoring and Analysis |
-
2019
- 2019-12-16 CN CN201911288393.XA patent/CN111062316A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3050300A1 (en) * | 2016-04-19 | 2017-10-20 | Thales Sa | METHOD AND DEVICE FOR AUTOMATIC DETECTION OF POLLUTION ZONES ON A WATER SURFACE |
| US20200125852A1 (en) * | 2017-05-15 | 2020-04-23 | Deepmind Technologies Limited | Action recognition in videos using 3d spatio-temporal convolutional neural networks |
| CN108007573A (en) * | 2017-12-04 | 2018-05-08 | 佛山市南海区环境保护监测站（佛山市南海区机动车排气污染管理所） | A kind of motor-vehicle tail-gas blackness analysis system and method |
| CN108009736A (en) * | 2017-12-13 | 2018-05-08 | 北京北华中清环境工程技术有限公司 | A kind of water quality early-warning and predicting system and water quality early-warning and predicting method |
| CN108881857A (en) * | 2018-08-04 | 2018-11-23 | 肖恒念 | Blowdown intelligent control method based on real-time video |
| CN109325403A (en) * | 2018-08-07 | 2019-02-12 | 广州粤建三和软件股份有限公司 | A kind of water pollution identification administering method and system based on image recognition |
| CN109886139A (en) * | 2019-01-28 | 2019-06-14 | 平安科技（深圳）有限公司 | Human testing model generating method, sewage draining exit method for detecting abnormality and device |
| CN110085281A (en) * | 2019-04-26 | 2019-08-02 | 成都之维安科技股份有限公司 | A kind of environmental pollution traceability system and method based on feature pollution factor source resolution |
| CN110220851A (en) * | 2019-06-25 | 2019-09-10 | 生态环境部卫星环境应用中心 | A kind of air pollution emission detection method and system based on unmanned plane |
| KR20190092327A (en) * | 2019-07-18 | 2019-08-07 | 엘지전자 주식회사 | Water purifier and control method thereof |
| US20210389293A1 (en) * | 2020-06-12 | 2021-12-16 | Chinese Research Academy Of Environmental Sciences | Methods and Systems for Water Area Pollution Intelligent Monitoring and Analysis |
| CN112712008A (en) * | 2020-12-28 | 2021-04-27 | 华川技术有限公司 | Water environment early warning judgment method based on 3D convolutional neural network |
Non-Patent Citations (2)
| Title |
|---|
| 刘豪: "浅析智能网络摄像机在环保监控中的应用", 《中国安防》 * |
| 秦榜辉等: "排污口有机污染物的GC/MS/AMDIS定性分析", 《海洋环境科学》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112101790A (en) * | 2020-09-16 | 2020-12-18 | 清华大学合肥公共安全研究院 | Water body monitoring video and data linkage early warning method and system |
| CN112101790B (en) * | 2020-09-16 | 2024-03-15 | 清华大学合肥公共安全研究院 | Water body monitoring video and data linkage early warning method and system |
| CN113049445B (en) * | 2021-03-22 | 2022-02-01 | 中国矿业大学(北京) | Coal water slurry fluidity detection device based on deep learning and detection method thereof |
| CN113049445A (en) * | 2021-03-22 | 2021-06-29 | 中国矿业大学(北京) | Coal water slurry fluidity detection device based on deep learning and detection method thereof |
| CN113128105A (en) * | 2021-03-31 | 2021-07-16 | 北京工业大学 | Method and device for monitoring sudden watershed water pollution accident |
| CN113128105B (en) * | 2021-03-31 | 2024-05-17 | 北京工业大学 | Method and device for monitoring sudden river basin water pollution accident |
| CN113343923A (en) * | 2021-07-01 | 2021-09-03 | 江苏舆图信息科技有限公司 | Real-time river drainage port drainage state identification method based on video images |
| CN113588650A (en) * | 2021-09-30 | 2021-11-02 | 武汉市中卫寰宇医疗系统工程有限公司 | Medical sewage purification monitoring system |
| CN113920471A (en) * | 2021-10-13 | 2022-01-11 | 平安国际智慧城市科技股份有限公司 | Remote monitoring method and device for production waste, computer equipment and storage medium |
| CN115100553A (en) * | 2022-07-06 | 2022-09-23 | 浙江科技学院 | River surface pollution information detection processing method and system based on convolutional neural network |
| CN115457485A (en) * | 2022-11-11 | 2022-12-09 | 成都见海科技有限公司 | Drainage monitoring method and system based on 3D convolution and storage medium |
| CN115620243A (en) * | 2022-12-20 | 2023-01-17 | 深圳联和智慧科技有限公司 | Pollution source monitoring method and system based on artificial intelligence and cloud platform |
| CN117475367A (en) * | 2023-06-12 | 2024-01-30 | 中国建筑第四工程局有限公司 | Sewage image processing method and system based on multi-rule coordination |
| CN117475367B (en) * | 2023-06-12 | 2024-05-07 | 中国建筑第四工程局有限公司 | Sewage image processing method and system based on multi-rule coordination |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111062316A (en) | Pollution source wastewater discharge real-time video analysis system based on deep learning technology | |
| Chu et al. | Multilayer hybrid deep‐learning method for waste classification and recycling | |
| Dong et al. | A deep-learning-based multiple defect detection method for tunnel lining damages | |
| CN103903008B (en) | A kind of method and system of the mist grade based on image recognition transmission line of electricity | |
| CN109033934A (en) | A kind of floating on water surface object detecting method based on YOLOv2 network | |
| CN102867195B (en) | Method for detecting and identifying a plurality of types of objects in remote sensing image | |
| Stumpf et al. | Quantifying Karenia brevis bloom severity and respiratory irritation impact along the shoreline of Southwest Florida | |
| CN108229524A (en) | A kind of chimney and condensing tower detection method based on remote sensing images | |
| NU | Automatic detection of texture defects using texture-periodicity and Gabor wavelets | |
| Aitsaadi et al. | Differentiated underwater sensor network deployment | |
| Li et al. | Vehicle detection in remote sensing images using denoizing-based convolutional neural networks | |
| CN108387692A (en) | A kind of atmosphere pollution intelligent monitor system | |
| CN109829426A (en) | Railway construction temporary building monitoring method and system based on high score remote sensing image | |
| Penmetcha et al. | Computer vision-based algae removal planner for multi-robot teams | |
| Kong et al. | Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images | |
| CN113469097B (en) | Multi-camera real-time detection method for water surface floaters based on SSD network | |
| CN103185731A (en) | Device for detecting beef tenderness based on color image textural features and method thereof | |
| Prabu et al. | Automated Crack and Damage Identification in Premises using Aerial Images based on Machine Learning Techniques | |
| Ramalingam et al. | Vision‐Based Dirt Detection and Adaptive Tiling Scheme for Selective Area Coverage | |
| Simoncelli et al. | A low‐cost underwater particle tracking velocimetry system for measuring in situ particle flux and sedimentation rate in low‐turbulence environments | |
| Cao et al. | Balanced multi-scale target score network for ceramic tile surface defect detection | |
| CN103065296A (en) | High-resolution remote sensing image residential area extraction method based on edge feature | |
| Lun et al. | Skip-YOLO: Domestic Garbage Detection Using Deep Learning Method in Complex Multi-scenes | |
| Ran et al. | Crack-SegNet: Surface Crack Detection in Complex Background Using Encoder-Decoder Architecture | |
| Roostaei | IoT Based Edge and Cloud Computing for Smart Environmental Engineering Applications |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200424 |